CC BY
4
Original articles Оригинальные статьи
Russian Journal of Infection and Immunity = Infektsiya i immunitet Инфекция и иммунитет
2023, vol. 13, no. 1, pp. 147-155 2023, Т. 13, № 1, с. 147-155
USE OF SOME BONE-RELATED CYTOKINES AS PREDICTORS FOR RHEUMATOID ARTHRITIS SEVERITY BY NEURAL NETWORK ANALYSIS
R.O. Saleha, L.A. Mahmood", M.A. Mohammedc, K.F. Al-Rawid, H.K. Al-Hakeime
aAl-Maarif University College, Ramadi, AlAnbar Governorate, Iraq b College of Medicine, University of Anbar, Ramadi, Al Anbar Governorate, Iraq c General Directorate of Anbar Education, Ministry of Education, Al Anbar, Iraq d College of Science, University of Anbar, Ramadi, Al Anbar Governorate, Iraq e College of Science, University of Kufa, Kufa, Najaf Governorate, Iraq
Abstract. Background. Rheumatoid arthritis (RA) is characterized by synovial membrane inflammation that results in joint damage. Many earlier studies have measured cytokines for a better diagnosis of RA. In the present study, three bone biomarkers [osteopontin, stromelysin-1 (MMP3), and vascular endothelial growth factor-A (VEGF)] are examined for their ability to estimate the severity of disease by using artificial neural network (NN) analysis and binary logistic regression analysis. Methods. The study enrolled 87 RA patients and 44 healthy control subjects. The biomarkers were measured by the enzyme-linked immunosorbent assay technique. Disease Activity Score (28 joints) and C-reactive protein (CRP) (DAS28-CRP) was calculated by using DAS28-CRP calculator. The patients with DAS28-CRP > 5.1 are considered as having high disease activity (HDA). While patients' group with DAS28-CRP < 5.1 are considered as moderate disease activity (MDA). The neural network (NN) analysis was used for the differentiation between groups. Results. Results showed that the most sensitive predictor for high disease activity (HDA) of RA is MMP3, followed by osteopontin and VEGF. These three biomarkers can differentiate significantly between HDA and MDA with a relatively high size effect (Partial n2 = 0.323, p < 0.001). The HDA group has a significantly higher MMP3, CRP, RF, and anti-citrullinated protein antibodies (ACPA) than the MDA group. MMP3 is strongly associated with two inflammatory indicators; CRP and ESR. Conclusion. There was a significant elevation in the serum level of MMP3 in RA patients with HDA compared to the MDA and control groups. High DAS28, RF, CRP, and ACPA were found in HDA patients compared with the MDA group. The use of the NN analysis indicated that the measured biomarkers help predict the HDA state in RA patients. MMP3 and osteopontin are diagnostic biomarkers for the severity of RA and are related to many disease-related characteristics with a sensitivity of 88.9% and specificity of 68.4%.
Key words: rheumatoid arthritis, inflammation, neural network analysis, stromelysin-1, ACPA, osteopontin.
Адрес для переписки:
Халид Ф. Аль-Рави
College of Science, University of Anbar, Ramadi, Al Anbar Governorate, Iraq. Тел.: +9647835303535. E-mail: sc.kfwi72@uoanbar.edu.iq
Для цитирования:
Салех Р.О., Махмуд Л.А., Мохаммед М.А., Аль-Рави Х.Ф., Аль-Хакейм Х.К. Использование ряда цитокинов, ассоциированных с костной тканью, в качестве предикторов тяжести ревматоидного артрита при помощи нейросетевого анализа // Инфекция и иммунитет. 2023. Т. 13, № 1. С. 147-155. Со1: 10.15789/2220-7619-1Ю8-2008
© 8а1еИ Я.О. et а1., 2023
Contacts:
Khalid F. Al-Rawi
College of Science, University of Anbar. Phone: +9647835303535. E-mail: sc.kfwi72@uoanbar.edu.iq
Citation:
Saleh R.O., Mahmood L.A., Mohammed M.A., Al-Rawi K.F., Al-Hakelm H.K. Use of some bone-related cytokines as predictors for rheumatoid arthritis severity by neural network analysis // Russian Journal of Infection and Immunity = Infektsiya i immunitet, 2023, vol. 13, no. 1, pp. 147-155. doi: 10.15789/2220-7619-UOS-2008
DOI: http://dx.doi.org/10.15789/2220-7619-U0S-2008
Статья доступна по лицензии Creative Commons Attribution 4.0
The article can be used under the Creative Commons Attribution 4.0 License
ИСПОЛЬЗОВАНИЕ РЯДА ЦИТОКИНОВ, АССОЦИИРОВАННЫХ С КОСТНОЙ ТКАНЬЮ, В КАЧЕСТВЕ ПРЕДИКТОРОВ ТЯЖЕСТИ РЕВМАТОИДНОГО АРТРИТА ПРИ ПОМОЩИ НЕЙРОСЕТЕВОГО АНАЛИЗА
Салех Р.О.1, Махмуд Л.А.2, Мохаммед М.А.3, Аль-Рави Х.Ф.4, Аль-Хакейм Х.К.5
1 Университетский колледж Аль-Маариф, г. Рамади, провинция Аль-Анбар, Ирак 2Медицинский колледж, Университет Анбар, г. Рамади, провинция Аль-Анбар, Ирак
3Главное управление образования провинции Анбар, Министерство образования, провинция Аль-Анбар, Ирак 4Научный колледж, Университет Анбар, г. Рамади, провинция Аль-Анбар, Ирак 5Научный колледж, Университет Куфы, г. Куфа, провинция Наджаф, Ирак
Резюме. История вопроса. Ревматоидный артрит (РА) характеризуется воспалением синовиальной оболочки, приводящего к повреждению суставов. Многие более ранние исследования оценивали уровень цитокинов для улучшения диагностики РА. В настоящем исследовании для оценки тяжести заболевания с использованием нейронной сети и бинарного логистического регрессионного анализа были исследованы три костных биомаркера: остеопонтин, стромелизин-1 (ММР3) и фактор роста эндотелия сосудов А (VEGF). Методы. В исследовании приняли участие 87 больных РА и 44 здоровых человека контрольной группы. Уровень биомаркеров определяли методом иммуноферментного анализа. Показатель активности заболевания (28 суставов) и С-реактивный белок (CRP) (DAS28-CRP) рассчитывали с помощью БЛ828-СКР-калькулятора. Пациенты с DAS28-CRP > 5,1 считаются имеющими высокую активность заболевания (ВАЗ), в то время как при DAS28-CRP < 5,1 заболевание расценивается как умеренно активное (УАЗ). Нейросетевой анализ использовался для дифференциации между группами. Результаты. Результаты исследования показали, что наиболее чувствительным предиктором высокой активности заболевания (HDA) РА является MMP3, за которым следуют остеопонтин и VEGF. Эти три биомаркера могут существенно дифференцировать HDA и MDA с относительно высокой эффективностью (частичный п2 = 0,323, p < 0,001). Группа с ВАЗ имеет значительно более высокий уровень MMP3, CRP, RF и антител к цитруллинированному белку (ACPA), чем группа с УАЗ. MMP3 тесно связан с двумя индикаторами воспаления: СРБ и СОЭ. Выводы. Отмечалось значительное повышение уровня ММР3 в сыворотке крови и уровней у пациентов с РА с ВАЗ по сравнению с группой с УАЗ и контрольной группой. Высокие уровни DAS28, RF, CRP и ACPA были обнаружены у пациентов с ВАЗ по сравнению с группой пациентов с УАЗ. Использование нейросетевого анализа показало, что измеренные биомаркеры помогают прогнозировать ВАЗ у пациентов с РА. MMP3 и остеопонтин являются диагностическими биомаркерами тяжести заболевания РА с чувствительностью 88,9% и специфичностью 68,4% и связаны со многими характеристиками заболевания.
Ключевые слова:ревматоидный артрит, воспаление, нейросетевой анализ, стромелизин-1, ACPA, остеопонтин.
Background
Rheumatoid arthritis (RA) usually begins between the ages of 30 and 50 years old. Women, smokers, and those with a family history of the disease are at higher risk. It causes discomfort and stiffness in various joints, most commonly the wrists, proximal interphalangeal joints, and metacarpophalangeal joints. RA rarely affects the distal interphalangeal or lumbar spine. Patients may also have weariness, weight loss, and anemia [55]. RA is an autoimmune-inflammatory illness that often affects females and older individuals. It is characterized by joint discomfort that develops with time as a result of an autoimmune-inflammatory response [34]. Despite the fact that the cause of RA is yet unknown, a set of environmental and genetic aspects is attributed [54]. A number of parameters have been evaluated as potential predictors of RA diagnosis, prognosis, or follow-up of RA disease. Because RA is characterized by an inflammation of the synovial membrane that leads to the reduction and destruction of joints [3, 36], adhesion molecules, soluble mediators, pro-inflammatory and anti-inflammatory cytokines [33, 53,
57], trace elements [1], adipokines [35] and various impacts of autoantibodies on joint inflammation and internal organ dysfunction, as well as structural abnormalities, are known [14]. Since the findings of the majority of criteria are not completely clear and definitive, the estimation of various inflammation-related cytokines continues to be an attractive subject of research. In the present work, the study focused on some of the less studied cytokines in RA including osteopontin (OPN), Stromelysin-1 (MMP3), and the vascular endothelial growth factor-A (VEGF). An OPN-rich extracellular matrix is present in mineralized tissues and extracellular fluids, especially at sites of inflammation [15, 61], where OPN combines osteoclasts and hydroxyapatite to enhance bone resorption [18]. In mice modeled with rheumatoid arthritis, OPN plays a critical role in the destruction of articular cartilage by promoting angiogenesis and inducing apoptosis in chondrocytes [60]. Earlier research showed a correlation between elevated levels of OPN and serum levels of inflammation markers, an increase in monocyte inflammatory molecules [63], and the number of T-helper 17 cells in the synovial fluid of RA patients [6, 37]. Furthermore,
in people with RA, plasma OPN was thought to be a biomarker of inflammatory bone damage [20]. Numerous studies have indicated that the OPN and its receptors, play key roles in RA pathogenesis [62].
The MMP3 enzyme is involved in the degradation of cartilage and bones and the destruction of extracellular matrix components in RA [41]. MMP3 level has been elevated in the synovium of RA patients where MMP3 concentration is around 250 times higher [7, 42]. A high MMP3 level correlates with the number of affected joints [59], and it could be used as a biomarker for both the diagnosis and the progress of the disease [7, 25]. MMP3 may be a biomarker for the disease, and it helps to break down extracellular matrix proteins in RA as the disease progresses. [39]. Serum levels of MMP3 were a sign of RA disease activity, bone and joint damage, medication susceptibility, and the outcome of the disease [25, 27]. After these findings, several researchers have recommended MMP3 testing as part of a routine examination to be used in conjunction with RA therapy choices [25].
There is an abundant expression of VEGF in synovial fluid and serum of RA patients [22] [58], which plays a key role in pannus formation and maintenance [58]. Patients with RA have high levels of VEGF in their blood and synovial fluid [58], which aids in the growth of blood vessels and their absorption by the synovial lining membrane in RA [31]. In a meta-analysis, significantly higher circulating VEGF levels in patients with RA were identified, and levels of VEGF are positively correlated with disease activity in RA [24]. The present study aims to use the abovementioned biomarkers (OPN, MMP3, and VEGF). in the current study to test the ability to differentiate between high disease activity (HDA) the moderate disease activity (MDA) of RA disease by using the artificial neural network analysis (NN) and binary and multivariate logistic analysis.
Methods
Participants. Eighty-seven RA patients (37 males and 50 females) and 44 age-matched healthy control subjects (18 males and 26 females) were recruited in the present case-control study. The samples were collected from the Ramadi Teaching Hospital in the Governorate of Anbar in Iraq, from June 2020 to January 2021. The European League Against Rheumatism and the American College of Rheumatology guidelines were used to diagnose RA in diseased subjects [2]. According to these diagnosis criteria, each patient should have a score > 6 based on the amount and location of painful joints, favorable serologic findings (anti-citrullinated protein antibodies (ACPA) and rheumatoid factor (RF)), high C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR)), and duration of RA symptoms. These criteria were found to be more accurate in predicting the likelihood of RA and have a higher
specificity [9]. The clinical characteristics and soci-odemographic data of all study subjects have been collected. Body mass index (BMI) was calculated by dividing subjects' body weight (kilograms) by their height squared (squared meter).
The Disease Activity Score was calculated by using (DAS28-CRP) calculator available online at https://www.mdcalc.com/disease-activity-score-28-rheumatoid-arthritis-crp-das28-crp. We divided patients into those with HDA when DAS28-CRP > 5.1 and those with MDA when DAS28-CRP < 5.1 [52]. The institutional review board (IRB) of the University of Anbar supplied the ethical approval of the study (Document number 211C/2020), which complied with the "International Guideline for Human Research" guidelines established by the Declaration of Helsinki. A thorough medical profile was taken on all participants to rule out any existing systemic disorders that may affect the results of the studied parameters, especially diabetes mel-litus, liver and kidney diseases, and cardiovascular incidents. The first step to ensure the work's quality is to recruit patients who have no kidney problems. All patients had normal urea and creatinine. These results are important to exclude any excretion of small-molecular weight proteins by diseased kidneys. Subjects that smoked were also excluded from the study.
Power analysis, using a 2-tailed test at a = 0.05 and assuming an effect size of 0.5 with a power of 0.80, shows that the required sample size is 127 participants as calculated by the sample size calculator (https://www.ai-therapy.com/psychology-statistics/ sample-size-calculator). Therefore, we used more than the calculated number (131 subjects). The sample size analysis was accomplished according to the equations and principles mentioned in Fahim et al. (2019) [11]. Patients were included by a convenience sampling technique.
Measurements. Five milliliters of venous blood were extracted without a tourniquet from all subjects, after overnight fasting, and centrifuged at 3000 rpm for 15 minutes after full clotting. Sera were separated and stored at —80°C until they were analyzed. Based on the latex agglutination concept, serum CRP and RF were assessed using semi-quantitative kits provided by the Spinreact® Co., Girona, Spain. Kits supplied by Hotgen Biotech Co., Ltd., Beijing, China, were used to conduct a semi-quantitative ACPA examination. Sandwich ELISA assay kits provided by Mybiosource®, Inc., CA, USA, were used to assess serum MMP3, OPN, and VEGF. All of the kits' in-tra-assay coefficients of variance were less than 10%. The sensitivities of the ELISA kits were OPN < 0.1 pg/ ml, MMP3 < 0.068 ng/ml, and VEGF < 1 pg/ml.
Statistical analysis. Kolmogorov—Smirnov test revealed that all biomarker findings were normally distributed. As a result, all data are provided in terms of mean and standard deviation. To compare meas-
ured variables across categories, the analysis of variance (ANOVA) test was performed. While the Chi-square (2) test was employed to determine the statistical significance of the difference between nominal variables. Pearson's correlation analysis was utilized to evaluate correlations between biomarkers and clinical and demographic variables. We used Point-Biserial Correlation Analysis to analyze the correlation between dichotomous variables (CRP, RF, and ACPA) and continuous variables (biomarkers). The "multivariate general linear model" (GLM) was used to examine associations between RA severity and measured biomarkers while adjusting for confounding factors such as age and BMI. The influence of each biomarker on the severity of rheumatoid arthritis was then determined using a between-subject effects test. The partial eta-squared (n2) effect size was used in the study. Based on the levels of the biomarkers, various z-unit weighted scores were determined. The essential biomarkers that predict indeed observed biomarkers were evaluated using multiple regression analysis. We have used NN processing to evaluate the predictability of the existence of RA in a subject using input variables and biomarkers. This analysis used an artificial feedforward model
of two hidden layers of mini-batch training and gradient descent. The stopping criteria were one successive move with no further decrease in the error expression. The research sample was split into three categories: training, testing, and holdout. The reference for the correlation coefficient is the values that are not due to the chance at p < 0.05. The correlation coefficient was regarded as low when it ranged from 0.10 to 0.29, moderate when it ranged from 0.30 to 0.49, and high when it ranged from 0.50 to 1.0 [8]. Area under the curve (AUC) cut-off value should be more than 0.5. The tests are two-tailed, with a statistical significance level of 0.05. The IBM SPSS package for Windows 10, Version 25, 2017, IBM Corp., New York, USA was used for biostatistical analysis.
Results
Demographic and clinical biomarkers. The results in Table 1 showed the demographic details of RA patients in comparison with those of healthy controls. There were no statistically significant variations in BMI or age between the groups. Table 1 also indicates that serum MMP3, RF, CRP, and ACPA levels were substantially higher in RA patients compared
Table 1. The clinical and demographic data in high disease activity (HDA) rheumatoid arthritis patients and moderate disease activity (MDA) and healthy controls (HC)
Variables HCA (n = 44) DAS < 5.1B (n = 34) DAS > 5.1C (n = 53) F/X2 P
Age, years 48.41±5.26 48.09±5.25 49.72±6.01 1.084 0.341
Sex (Male/Female) 18/26 13/21 24/29 1.147 0.292
MIB, kg/m2 24.38±2.90 25.70±2.86 25.59±3.19 2.533 0.083
DAS28 N/A 3.81±0.79C 7.70±0.74B 185.254 < 0.001
MMP-3, ng/ml 11.50±5.01BC 17.31±8.20AC 22.29±9.76AB 21.712 < 0.001
VEGF, pg/ml 202.97±56.35BC 128.92±42.82A 132.29±45.35A 31.941 < 0.001
OPN,ng/ml 3.03±1.20C 3.82±1.66 4.25±1.87A 6.897 0.001
Disease duration, years 0B,C 12.65±4.71A 11.06±5.13A 123.133 < 0.001
CRP -/+ 44/0BC 29/5AC 19/34AB 52.305 < 0.001
RF -/+ 44/0BC 28/6AC 20/33AB 47.806 < 0.001
ACPA -/+ 44/0BC 28/6AC 20/33AB 47.806 < 0.001
Tests Dependent variables Explanatory variables P Partial n2
Multivariate All 3 biomarkers Severity (HDA vs MDA) < 0.001 0.323
Sex 0.352 0.102
BMI 0.002 0.163
Age 0.152 0.063
Between-subject effects MMP3 Severity (HDA vs MDA) 0.002 0.161
VEGF Severity (HDA vs MDA) 0.249 0.037
Osteopontin Severity (HDA vs MDA) 0.162 0.023
Note. Diagnosis: RA versus healthy controls, BMI: Body mass index, MMP3: Matrix metalloproteinase-3, and VEGF: Vascular endothelial growth factor.
Notes. A, B, C: pair-wise comparisons (when a letter written on a number, it means that this number is significantly different from the values of the column that contain the letter over the column title), BMI: Body mass index, DAS28: Disease Activity Score-28, MMP3 (Stromelysin-1): Matrix metalloproteinase-3, VEGF: Vascular endothelial growth factor, RF: rheumatoid arthritis, CRP: C-reactive protein, ACPA: and anti-citrullinated protein antibodies, and OPN: osteopontin. The difference is considered significant when p < 0.05.
Table 2. The results of multivariate GLM analysis examining the differences in biomarkers between RA patients divided into those with high disease activity (HDA) and moderate disease activity (MDA)
Table 3. The binary logistic regression analysis results with high disease activity (HDA) and moderate disease activity (MDA) as dependent variables and bone biomarkers as explanatory variables
Dependent variables Explanatory variables* B (SE) p OR 95% CI
MDA versus HDA MMP-3 0.107 (0.037) 0.004 1.113 1.036-1.197
VEGF 0.011 (0.007) 0.138 1.011 0.997-1.025
Osteopontin 0.450 (0.168) 0.007 1.568 1.128-2.180
Note. (*): Standardized values, OR: Odd ratio, SE: standard error, CI: confidence Interval, MMP3: Matrix metalloprotelnase-3, VEGF: Vascular endothelial growth factor.
to the control group with the highest level in DAS
> 5.1 patients' group. Serum VEGF showed a significant decrease in both patient groups in comparison with the control groups. While no significant difference between both patient groups. Patients with DAS
> 5.1 have significantly higher OPN than those with DAS < 5.1 and control groups. However, there was no substantial difference in the duration of disease between RA patient groups.
The results of multivariate GLM. Table 2 shows the effects of the multivariate GLM study, which showed that age (Partial n2 = 0.063, p = 0.152) and sex (Partial n2 = 0.102, p = 0.352) have no substantial impact on the three biomarkers. BMI had a slight effect on the levels of the three biomarkers (Partial n2 = 0.163, p = 0.002). The severity of RA has a highly significant effect (Partial n2 = 0.323, p < 0.001) on the biomarker levels. Between-subject tests revealed that the severity had the greatest impact on MMP3 (partial n2 = 0.161, p = 0.002). OPN and VEGF demonstrated insignificant effects on the severity of the disease with a very small effect size.
Intercorrelation matrix. The most notable correlations were a significant moderate negative correlation between MMP3 and ACPA (r = -0.379, p < 0.01). The duration of illness is moderately related to VEGF (r = 0.325, p < 0.01) and OPN (r = 0.383, p < 0.001). CRP has a moderate negative correlation with VEGF (r = -0.357, p < 0.01) (results are not tabulated).
Results of binary logistic regression analysis. The binary logistic regression test recruited HDA state as a dependent variable (and MDA as the reference group) is seen in Table 3. The regression discriminated severity of RA patients and found that MMP3 and OPN substantially discriminated both study groups (%2 = 20.116, df = 3, p < 0.001). The Nagelkerke's effect size was 0.350, and the classification precision was 74.2%, with a sensitivity of 66.7% and a specificity of 81.8%.
Effects of background variables. The univariate GLM analysis was used to assess the impact of medications on the serum levels of the studied parameters in RA patients. The study found no significant effects of naproxen, sulfasalazine, prednisolone, tofacitinib, or methotrexate on the blood levels of the three bio-markers tested. The other medications prescribed have little discernible impact. The cumulative effects of medication administration on the assessed parameters were just minor (partial n2 = 0.033) (results are not tabulated).
Results of neural networks. As shown in Table 4, the neural network analysis was used to distinguish between individuals with HDA and MDA. One unit was employed in hidden layer 1, whereas hyperbolic tangent was used as an activation function, identity in the output layer, and sum of squares was used as an error term in the final neural network. Because the testing set had a significantly smaller sum of squares (5.331) and a lower relative error (23.6 percent versus 47.8 percent, respectively), the neural network model was able to generalize the trend. As a result, the holdout set had a 66.7 percent relative inaccuracy. As seen in Table 4, the sensitivity was 88.9%, and the specificity was 68.4%. The cut-off values of the serum biomarkers concentration at the above sensitivity and specificity were: MMP3 > 17.75 ng/ ml, VEGF > 124.95 pg/ml, OPN > 3.814 ng/ml. As shown in Figure, the input variables are ranked by relevance and relative importance, MMP3, OPN, and VEGF were the three most important predictors of the model's predictive power.
Table 4. The Results of neural networks with high disease activity (HDA) RA versus moderate disease activity (MDA) as reference group
Models HDA vs MDA
Input Number of units 3
layer Rescaling method Normalized
Number of hidden layers 1
Hidden Number of units in hidden 3
layers layer 1
Activation Function Hyperbolic tangent
Dependent variables HDA vs MDA
Output Number of units 2
layer Activation function Identity
Error function Sum of squares
Sum of squares error term 10.358
Training % Incorrect or relative error 23.6%
Prediction (sensitivity, specificity) 75.0%, 69.6%
Sum of Squares error 5.331
% Incorrect or relative error 47.8%
Testing Prediction (sensitivity, specificity) 88.9%, 68.4%
AUC ROC 0.81
% Incorrect or relative error 66.7%
Holdout Prediction (sensitivity, specificity) 60.0%, 45.5%
Note. AUC ROC: area under the curve of receiver operating curve.
Normalized importance 0% 20% 40% 60% 80% 100%
MMP3
OPN
VEGF
0.0 0.1 0.2 0.3 0.4 0.5
Importance
Figure. The importance of the biomarkers for differentiation between severity states of the rheumatoid arthritis patients by neural network analysis
Discussion
The current study's major finding is the elevation in serum MMP3 in RA patients when compared with the control group as seen in Table 1. The increase of MMP3 in RA is in accordance with other previous works [27, 29, 32, 48] that can be attributed to the secretion of MMP3, in higher amounts, from the synovial joints [42]. The contribution of pro-inflammatory cytokines in RA's pathogenesis is widely accepted [16]. Therefore, MMP3 has been proposed as a marker of inflammation in RA [41]. However, at high levels, MMP3 causes increases in the number of osteoclast precursors and osteoclast formation, resulting in inducing bone resorption [5]. Furthermore, MMP3 is correlated with the number of inflamed and painful joints in RA patients [59]. Serum MMP3 may be a possible diagnostic for histological synovi-tis and RA diagnosis [28], joint erosions in the early stages of the illness, and disease progression monitoring [26, 48]. MMP3 levels in the blood are an excellent predictor of bone damage, and MMP3 inhibition may be a significant treatment approach for individuals with early rheumatoid arthritis [47]. According to recent research, serum MMP3 levels predict clinical remission more accurately than CRP levels [17]. These results prompted several researchers to recommend MMP3 testing as part of a standardized evaluation to go along with RA treatment options [25].
In addition, a neural network method allowed for external validation of the clinical diagnosis of the HDA of RA against MDA with an AUC ROC curve of 0.814 and MMP3 and OPN as the most important discriminatory variable. Macrophage populations in synovial tissue are associated with articular damage, and a decrease in macrophage counts is a sensitive biomarker of therapy response in rheumatoid arthritis patients [56].
The multivariate GLM analysis was performed to estimate the effect of the cofounders on the serum levels of the measured parameters. As seen in Table 2,
age and sex had no substantial impact on the levels of the assessed biomarkers. BMI has a slight effect on the variance of the serum level of the measured bone-related cytokines. The levels of biomarkers are substantially altered only by the magnitude of the severity in the RA patients, with a larger effect size (partial n2 = 0.323). Tests for between-subject effects showed that 16.1 percent of the difference in the MMP3 concentration was attributed to RA. These analyses were performed to rule out the influence of cofounders on the values of the estimated parameters because there are some reports about the effect of BMI on MMP3 level [4], decreased expression of VEGF with increased patient age [12], and the effect of sex and age on OPN level [21]. However, these correlations were reported in diseases other than RA.
MMP3 is secreted by synovial fibroblasts or B cells, which are well-known MMP3 producers, that are responsible for the large rise in plasma levels [46]. The previously published study demonstrated a tight link between plasma OPN levels and MMP3 levels. Plasma OPN levels decreased considerably in re-sponders after medication [20].
Table 3 showed that the severity of the RA is associated with the plasma level of MMP3. MMP3 is strongly associated with two inflammatory indicators; CRP and ESR [45]. These findings revealed a correlation between the levels of bone-related cy-tokines and biomarkers of inflammation. Other researchers, however, demonstrated that elevated serum biomarkers could not be a risk factor for reduced bone mineral density [44]. The same reasons apply: the inflammatory state associated with RA is the primary source of biomarker changes. ACPA levels are influenced by serum MMP3 levels. Previously, ACPA was found to have the best predictive importance for the production of RA [38, 51]. A strong association between ACPA positivity and arthritis development has also been established in several patients who later experienced RA [40]. Therefore, the increase in serum MMP3 may enforce the validity and sensitivity of ACPA in the diagnosis of RA in addition to estimating the severity of the RA disease [40].
There is a good predictive value of the serum MMP3 for the severity of RA subjects. MMP3 is a connective tissue biomarker. However, the diagnostic cut-off value is rather high. MMP3 expression is a good indicator of disease activity in people with rheumatoid arthritis [30]. MMP3 levels rose with advanced stage and RA class and gradually dropped following treatment effectiveness [49]. Serum MMP3 levels were shown to be strongly linked with serum CRP, RF levels, and joint damage [26]. Additionally, there was a statistically significant association between MMP3 and CRP, and ESR [17]. Elevated blood MMP3 levels in rheumatoid arthritis patients show inflammation [10, 32] and serve as an early predictor of increasing joint destruction as well as a powerful predictive indicator of rheumatoid
arthritis disease activity [10, 29]. In another study, serum concentrations of MMPs significantly correlated with markers of RA activity such as DAS28 and CRP levels [23].
Recently, it was shown that MMP3 serum levels correspond with the quantity of MMP3 generated by synovial mast cells [19] in inflamed joints, indicating the severity of rheumatoid synovitis [13]. With increasing RA severity, MMP3 and ACPA increase indicating the correlation of both biomarkers with the degree of disease activity [43, 50]. Therefore, the measured biomarkers, especially MMP3 can be used as a potential biomarker for RA activity with acceptable sensitivity and specificity. The main limitation of the current analysis is the limited size of the test sample. To ensure adequate generalization of the
References
study findings, a greater sample size would be needed. The second drawback is the relatively high inter-assay coefficient of variance percent, which was less than 10% for all kits.
Conclusion
There was a significant elevation in the serum level of MMP3 in RA patients with HDA compared to the MDA and control groups. The NN analysis showed that the MMP3 is the most sensitive predictor for RA patients with HDA followed by OPN. In addition to the known high DAS28, RF, CRP, and ACPA in HDA patients, the binary logistic regression showed the potential use of OPN and MMP3 as differentiating factors for HDA from MDA.
1. Al-Hakeim H.K., Moustafa S.R., Jasem K.M. Serum cesium, rhenium, and rubidium in rheumatoid arthritis patients. Biol. Trace Elem. Res, 2019, vol. 189, no. 2,pp. 379-386. doi: 10.1007/s12011-018-1497-5
2. Aletaha D., Neogi T., Silman A.J., Funovits J., Felson D.T., Bingham III CO, Birnbaum N.S., Burmester G.R., Bykerk V.P., Cohen M.D. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum, 2010, vol. 62, no. 9, pp. 2569-2581. doi: 10.1136/ard.2010.138461
3. Anandarajah A.P. Clinical aspects of rheumatoid arthritis: highlights from the 2010 ACR conference. Int. J. Clin. Rheumatol, 2011, vol. 6, no. 3:267. doi: 10.2217/IJR.11.39
4. Boumiza S., Chahed K., Tabka Z., Jacob M.-P., Norel X., Ozen G. MMPs and TIMPs levels are correlated with anthropometric parameters, blood pressure, and endothelial function in obesity. Scientific Reports, 2021, vol. 11, no. 1:20052. doi: 10.1038/s41598-021-99577-2
5. Cessak G., Kuzawinska O., Burda A., Lis K., Wojnar M., Mirowska-Guzel D., Balkowiec-Iskra E. TNF inhibitors — mechanisms of action, approved and off-label indications. Pharmacol. Rep., 2014, vol. 66, no. 5,pp. 836-844. doi: 10.1016/j.pharep.2014.05.004
6. Chen G., Zhang X., Li R., Fang L., Niu X., Zheng Y., He D., Xu R., Zhang J.Z. Role of osteopontin in synovial Th17 differentiation in rheumatoid arthritis. Arthritis Rheum., 2010, vol. 62, no. 10, pp. 2900-2908. doi: 10.1002/art.27603
7. Chen J.J., Huang J.F., Du W.X., Tong P.J. Expression and significance of MMP3 in synovium ofknee joint at different stage in osteoarthritis patients. Asian Pac. J. Trop. Med, 2014, vol. 7, no. 4, pp. 297-300. doi: 10.1016/S1995-7645(14)60042-0
8. Cohen J., Cohen P., West S.G., Aiken L.S. Applied multiple regression/correlation analysis for the behavioral sciences; 3rd edition. Taylor and Francis, 2013, 536p.
9. Cornec D., Varache S., Morvan J., Devauchelle-Pensec V., Berthelot J.M., Le Henaff-Bourhis C., Hoang S., Martin A., Chales G., Jousse-Joulin S., Saraux A. Comparison of ACR 1987 and ACR/EULAR 2010 criteria for predicting a 10-year diagnosis of rheumatoid arthritis. Joint Bone Spine, 2012, vol. 79, no. 6, pp. 581-585. doi: 10.1016/j.jbspin.2012.01.015
10. Fadda S., Abolkheir E., Afifi R., Gamal M. Serum matrix metalloproteinase-3 in rheumatoid arthritis patients: Correlation with disease activity and joint destruction. Egyptian Rheumatol., 2016, vol. 38, no. 3, pp. 153-159. doi: 10.1016/j.ejr.2016.01.001
11. Fahim N.K., Negida A., Fahim A.K. Sample Size Calculation Guide — Part 3: How to calculate the sample size for an independent case-control study. Adv. J. Emerg. Med., 2019, vol. 3, no. 2: e20. doi: 10.22114/AJEM.v0i0.138
12. Fane M.E., Ecker B.L., Kaur A., Marino G.E., Alicea G.M., Douglass S.M., Chhabra Y., Webster M.R., Marshall A., Colling R., Espinosa O., Coupe N., Maroo N., Campo L., Middleton M.R., Corrie P., Xu X., Karakousis G.C., Weeraratna A.T. sFRP2 supersedes VEGF as an age-related driver of angiogenesis in melanoma, affecting response to anti-VEGF therapy in older patients. Clin. Cancer. Res, 2020, vol. 26, no. 21, pp. 5709-5719. doi: 10.1158/1078-0432.CCR-20-0446
13. Forootanfar H., Adeli-Sardou M., Nikkhoo M., Mehrabani M., Amir-Heidari B., Shahverdi A.R., Shakibaie M. Antioxidant and cytotoxic effect of biologically synthesized selenium nanoparticles in comparison to selenium dioxide. J. Trace Elem. Med. Biol, 2014, vol. 28, no. 1,pp. 75-79. doi: 10.1016/j.jtemb.2013.07.005
14. Giannini D., Antonucci M., Petrelli F., Bilia S., Alunno A., Puxeddu I. One year in review 2020: pathogenesis of rheumatoid arthritis. Clin. Exp. Rheumatol., 2020, vol. 38, no. 3, pp. 387-397.
15. Gravallese E.M. Osteopontin: a bridge between bone and the immune system. J. Clin. Invest., 2003, vol. 112, no. 2, pp. 147-149. doi: 10.1172/JCI19190
16. Guo X., Wang S., Godwood A., Close D., Ryan P.C., Roskos L.K., White W.I. Pharmacodynamic biomarkers and differential effects of TNF- and GM-CSF-targeting biologics in rheumatoid arthritis. Int. J. Rheum. Dis., 2019, vol. 22, no. 4, pp. 646-653. doi: 10.1111/1756-185X13395
17. Hattori Y., Kida D., Kaneko A. Normal serum matrix metalloproteinase-3 levels can be used to predict clinical remission and normal physical function in patients with rheumatoid arthritis. Clin. Rheumatol., 2019, vol. 38, no. 1, pp. 181-187. doi: 10.1007/ s10067- 017-3829-9
18. Horton M.A., Nesbit M.A., Helfrich M.H. Interaction of osteopontin with osteoclast integrins. Ann. NY Acad. Sci., 1995, vol. 760, pp. 190-200. doi: 10.1111/j.1749-6632.1995.tb44630.x
19. Hueber A.J., Asquith D.L., Miller A.M., Reilly J., Kerr S., Leipe J., Melendez A.J., McInnes I.B. Cutting edge: mast cells express IL-17A in rheumatoid arthritis synovium. J. Immunol, 2010, vol. 184, no. 7, pp. 3336-3340. doi: 10.4049/jimmunol.0903566
20. Iwadate H., Kobayashi H., Kanno T., Asano T., Saito R., Sato S., Suzuki E., Watanabe H., Ohira H. Plasma osteopontin is correlated with bone resorption markers in rheumatoid arthritis patients. Int. J. Rheum. Dis., 2014, vol. 17, no. 1, pp. 50—56. doi: 10.1111/1756-185X.12115
21. Kariuki S.N., Moore J.G., Kirou K.A., Crow M.K., Utset T.O., Niewold T.B. Age- and gender-specific modulation of serum osteopontin and interferon-alpha by osteopontin genotype in systemic lupus erythematosus. Genes Immun., 2009, vol. 10, no. 5, pp. 487-494. doi: 10.1038/gene.2009.15.
22. Kim H.R., Kim K.W., Kim B.M., Cho M.L., Lee S.H. The effect of vascular endothelial growth factor on osteoclastogenesis in rheumatoid arthritis. PLoS One, 2015, vol. 10, no. 4: e0124909. doi: 10.1371/journal.pone.0124909
23. Klimiuk P.A., Domyslawska I., Sierakowski S., Chwiecko J. Regulation of serum matrix metalloproteinases and tissue inhibitor of metalloproteinases-1 following rituximab therapy in patients with rheumatoid arthritis refractory to anti-tumor necrosis factor blockers. Rheumatol. Int., 2015, vol. 35, no. 4, pp. 749-755. doi: 10.1007/s00296-014-3112-1
24. Lee Y.H., Bae S.C. Correlation between circulating VEGF levels and disease activity in rheumatoid arthritis: a meta-analysis. Z. Rheumatol, 2018, vol. 77, no. 3,pp. 240-248. doi: 10.1007/s00393-016-0229-5
25. Lerner A., Neidhofer S., Reuter S., Matthias T. MMP3 is a reliable marker for disease activity, radiological monitoring, disease outcome predictability, and therapeutic response in rheumatoid arthritis. Best Pract. Res. Clin. Rheumatol., 2018, vol. 32, no. 4, pp. 550-562. doi: 10.1016/j.berh.2019.01.006
26. Li L., Cai B., Liao J., Yang B., Huang Z., Wang L. [Clinical value of serum matrix metalloproteinase-3 in evaluating joint destruction and therapeutic effect in rheumatoid arthritis patients]. XiBao Yu Fen ZiMian YiXueZaZhi, 2013, vol. 29, no. 9, pp. 966-969.
27. Liu M.K., Wang L.C., Hu F.L. [Value of serum matrix metalloproteinase 3 in the assessment of early rheumatoid arthritis]. Beijing Da Xue Xue Bao Yi Xue Ban, 2018, vol. 50, no. 6, pp. 981-985.
28. Ma J.-D., Zhou J.-J., Zheng D.-H., Chen L.-F., Mo Y.-Q., Wei X.-N., Yang L.-J., Dai L. Serum matrix metalloproteinase-3 as a noninvasive biomarker of histological synovitis for diagnosis of rheumatoid arthritis. Mediat. Inflamm., 2014, vol. 2014:179284. doi: 10.1155/2014/179284
29. Ma J., Wang X., Mo Y., Chen L., Zheng D., Wei X., Dai L. [Value of serum matrix metalloproteinase-3 in the assessment of active disease in patients with rheumatoid arthritis]. Zhonghua Yi Xue Za Zhi, 2015, vol. 95, no. 47, pp. 3823-3828.
30. Ma M.J., Liu H.C., Qu X.Q., Wang J.L. Matrix metalloproteinase-3 gene polymorphism and its mRNA expression in rheumatoid arthritis. Genet. Mol. Res, 2015, vol. 14, no. 4, pp. 15652-9. doi: 10.4238/2015.December.1.17
31. Malemud C.J. Growth hormone, VEGF and FGF: involvement in rheumatoid arthritis. Clin. Chim. Acta, 2007, vol. 375, no. 1-2, pp. 10-19. doi: 10.1016/j.cca.2006.06.033
32. Mamehara A., Sugimoto T., Sugiyama D., Morinobu S., Tsuji G., Kawano S., Morinobu A., Kumagai S. Serum matrix metal-loproteinase-3 as predictor of joint destruction in rheumatoid arthritis, treated with non-biological disease modifying antirheumatic drugs. Kobe J. Med. Sci., 2010, vol. 56, no. 3, pp. E98-107.
33. Mateen S., Zafar A., Moin S., Khan A.Q., Zubair S. Understanding the role of cytokines in the pathogenesis of rheumatoid arthritis. Clin. Chim. Acta, 2016, vol. 455, pp. 161-171. doi: 10.1016/j.cca.2016.02.010
34. Miossec P. Rheumatoid arthritis: still a chronic disease. Lancet, 2013, vol. 381, no. 9870, pp. 884-886. doi: 10.1016/S0140-6736(12)62192-8
35. Mohammed Ali D.M., Al-Fadhel S.Z., Al-Ghuraibawi NHA, Al-Hakeim H.K. Serum chemerin and visfatin levels and their ratio as possible diagnostic parameters of rheumatoid arthritis. Reumatologia, 2020, vol. 58, no. 2, pp. 67-75. doi: 10.5114/reum.2020.95359
36. Mu N., Gu J.T., Huang T.L., Liu N.N., Chen H., Bu X., Zheng Z.H., Jia B., Liu J., Wang B.L., Wang Y.M., Zhu Z.F., Zhang Y., Zhang Y.Q., Xue X.C., Li M., Zhang W. Blockade of discoidin domain receptor 2 as a strategy for reducing inflammation and joint destruction in rheumatoid arthritis via altered interleukin-15 and dkk-1 signaling in fibroblast-like synoviocytes. Arthritis Rheumatol, 2020, vol. 72, no. 6, pp. 943-956. doi: 10.1002/art.41205
37. Ohshima S., Yamaguchi N., Nishioka K., Mima T., Ishii T., Umeshita-Sasai M., Kobayashi H., Shimizu M., Katada Y., Wakitani S., Murata N., Nomura S., Matsuno H., Katayama R., Kon S., Inobe M., Uede T., Kawase I., Saeki Y. Enhanced local production of osteopontin in rheumatoid joints. J. Rheumatol., 2002, vol. 29, no. 10, pp. 2061-2067.
38. Orr C., Najm A., Biniecka M., McGarry T., Ng C.T., Young F., Fearon U., Veale D.J. Synovial immunophenotype and anti-citrullinated peptide antibodies in rheumatoid arthritis patients: relationship to treatment response and radiologic prognosis. ArthritisRheum, 2017, vol. 69, no. 11, pp. 2114-2123. doi: 10.1002/art.40218
39. Page-McCaw A., Ewald A.J., Werb Z. Matrix metalloproteinases and the regulation of tissue remodelling. Nat. Rev. Mol. Cell Biol, 2007, vol. 8, no. 3, pp. 221-233. doi: 10.1038/nrm2125
40. Rakieh C., Nam J.L., Hunt L., Hensor E.M., Das S., Bissell L.A., Villeneuve E., McGonagle D., Hodgson R., Grainger A., Wakefield R.J., Conaghan P.G., Emery P. Predicting the development of clinical arthritis in anti-CCP positive individuals with non-specific musculoskeletal symptoms: a prospective observational cohort study. Ann. Rheum. Dis., 2015, vol. 74, no. 9, pp. 1659-1666. doi: 10.1136/annrheumdis-2014-205227
41. Ribbens C., Martin y Porras M., Franchimont N., Kaiser M.J., Jaspar J.M., Damas P., Houssiau F.A., Malaise M.G. Increased matrix metalloproteinase-3 serum levels in rheumatic diseases: relationship with synovitis and steroid treatment. Ann. Rheum. Dis, 2002, vol. 61, no. 2,pp. 161-166. doi: 10.1136/ard.61.2.161
42. Sasaki S., Iwata H., Ishiguro N., Obata K., Miura T. Detection of stromelysin in synovial fluid and serum from patients with rheumatoid arthritis and osteoarthritis. Clin. Rheumatol, 1994, vol. 13, no. 2, pp. 228-233. doi: 10.1007/BF02249017
43. Schuerwegh A.J., Ioan-Facsinay A., Dorjee A.L., Roos J., Bajema I.M., van der Voort E.I., Huizinga T.W., Toes R.E. Evidence for a functional role of IgE anticitrullinated protein antibodies in rheumatoid arthritis. Proc. Natl Acad. Sci. USA, 2010, vol. 107, no. 6, pp. 2586-2591. doi: 10.1073/pnas.0913054107
44. Sponholtz T.R., Zhang X., Fontes J.D., Meigs J.B., Cupples L.A., Kiel D.P., Hannan M.T., McLean R.R. Association between inflammatory biomarkers and bone mineral density in a community-based cohort of men and women. Arthritis Care Res. (Hoboken), 2014, vol. 66, no. 8, pp. 1233-1240. doi: 10.1002/acr.22270
45. Sun S., Bay-Jensen A.-C., Karsdal M.A., Siebuhr A.S., Zheng Q., Maksymowych W.P., Christiansen T.G., Henriksen K. The active form of MMP-3 is a marker of synovial inflammation and cartilage turnover in inflammatory joint diseases. BMC Musculoskelet. Disord, 2014, vol. 15, no. 93, pp. 1-12. doi: 10.1186/1471-2474-15-93
46. Tetlow L.C., Lees M., Ogata Y., Nagase H., Woolley D.E. Differential expression of gelatinase B (MMP-9) and stromely-sin-1 (MMP-3) by rheumatoid synovial cells in vitro and in vivo. Rheumatol. Int., 1993, vol. 13, no. 2, pp. 53-59. doi: 10.1007/ BF00307734
47. Tokai N., Yoshida S., Kotani T., Yoshikawa A., Kimura Y., Fujiki Y., Matsumura Y., Takeuchi T., Makino S., Arawaka S. Serum matrix metalloproteinase 3 levels are associated with an effect of iguratimod as add-on therapy to biological DMARDs in patients with rheumatoid arthritis. PLoS One, 2018, vol. 13, no. 8: e0202601. doi: 10.1371/journal.pone.0202601
48. Tuncer T., Kaya A., Gulkesen A., Kal G.A., Kaman D., Akgol G. Matrix metalloproteinase-3 levels in relation to disease activity and radiological progression in rheumatoid arthritis. Adv. Clin. Exp. Med., 2019, vol. 28, no. 5, pp. 665- 670. doi: 10.17219/ acem/94065
49. Uemura Y., Hayashi H., Takahashi T., Saitho T., Umeda R., Ichise Y., Sendo S., Tsuji G., Kumagai S. [MMP-3 as a biomarker of disease activity of rheumatoid arthritis]. Rinsho Byori, 2015, vol. 63, no. 12, pp. 1357-1364.
50. Umeda N., Matsumoto I., Sumida T. [The pathogenic role of ACPA in rheumatoid arthritis]. Nihon Rinsho Meneki GakkaiKaishi, 2017, vol. 40, no. 6, pp. 391-395. doi: 10.2177/jsci.40.391
51. Van de Stadt L.A., de Koning M.H., van de Stadt R.J., Wolbink G., Dijkmans B.A., Hamann D., van Schaardenburg D. Development of the anti-citrullinated protein antibody repertoire prior to the onset of rheumatoid arthritis. Arthritis Rheum., 2011, vol. 63, no. 11, pp. 3226-3233. doi: 10.1002/art.30537
52. Van Riel P., Renskers L. The Disease Activity Score (DAS) and the Disease Activity Score using 28 joint counts (DAS28) in the management of rheumatoid arthritis. Clin. Exp. Rheumatol., 2016, vol. 34, no. 5(suppl. 101), pp. S4-S40.
53. Vasilev G., Manolova I., Ivanova M., Stanilov I., Miteva L., Stanilova S. The role of IL-18 in addition to Th17 cytokines in rheumatoid arthritis development and treatment in women. Sci. Rep., 2021, vol. 11, no. 1:15391. doi: 10.1038/s41598-021-94841-x
54. Vickers N.J. Animal communication: when i'm calling you, will you answer too? Curr. Biol, 2017, vol. 27, no. 14, pp. R713-R715. doi: 10.1016/j.cub.2017.05.064
55. Wasserman A. Rheumatoid arthritis: common questions about diagnosis and management. Am. Fam. Physician., 2018, vol. 97, no. 7, pp. 455-462.
56. Wijbrandts C.A., Vergunst C.E., Haringman J.J., Gerlag D.M., Smeets T.J., Tak P.P. Absence of changes in the number of synovial sublining macrophages after ineffective treatment for rheumatoid arthritis: implications for use of synovial sublining macrophages as a biomarker. Arthritis Rheumatol., 2007, vol. 56, no. 11, pp. 3869-3871. doi: 10.1002/art.22964
57. Wu J., Li Q., Deng J., Zhao J.J., Yu Q.H. Association between IL-33 and other inflammatory factors in patients with rheumatoid arthritis and in fibroblast-like synoviocytes in vitro. Exp. Ther. Med., 2021, vol. 21, no. 2:161. doi: 10.3892/etm.2020.9592
58. Xu J., Feng Z., Chen S., Zhu J., Wu X., Chen X., Li J. Taxol alleviates collagen-induced arthritis in mice by inhibiting the formation of microvessels. Clin. Rheumatol, 2019, vol. 38, no. 1, pp. 19-27doi: 10.1007/s10067-017-3646-1
59. Yoshihara Y., Obata Ki, Fujimot N., Yamashita K., Hayakawa T., Shimmei M. Increased levels of stromelysin-1 and tissue inhibitor of metalloproteinases-1 in sera from patients with rheumatoid arthritis. Arthritis Rheumatol., 1995, vol. 38, no. 7, pp. 969-975. doi: 10.1002/art.1780380713
60. Yumoto K., Ishijima M., Rittling S.R., Tsuji K., Tsuchiya Y., Kon S., Nifuji A., Uede T., Denhardt D.T., Noda M. Osteopontin deficiency protects joints against destruction in anti-type II collagen antibody-induced arthritis in mice. Proc. Natl Acad. Sci. USA, 2002, vol. 99, no. 7,pp. 4556-4561. doi: 10.1073/pnas.052523599
61. Zhang F.-J., Gao S.-G., Cheng L., Tian J., Xu W.-S., Luo W., Song Y., Yang Y., Lei G.-H. The effect of hyaluronic acid on osteopontin and CD44 mRNA of fibroblast-like synoviocytes in patients with osteoarthritis of the knee. Rheumatol Int., 2013, vol. 33, no. 1, pp. 79-83. doi: 10.1007/s00296-011-2339-3
62. Zhang F., Luo W., Li Y., Gao S., Lei G. Role of osteopontin in rheumatoid arthritis. Rheumatol. Int., 2015, vol. 35, no. 4, pp. 589595. doi: 10.1007/s00296-014-3122-z
63. Zheng W., Li R., Pan H., He D., Xu R., Guo T.B., Guo Y., Zhang J.Z. Role of osteopontin in induction of monocyte chemoat-tractant protein 1 and macrophage inflammatory protein 1|3 through the NF-kB and MAPK pathways in rheumatoid arthritis. Arthritis Rheum, 2009, vol. 60, no. 7, pp. 1957-1965. doi: 10.1002/art.24625
Авторы:
Салех Р.О., отдел медицинской лабораторной техники Университетского колледжа Аль-Маариф, г. Рамади, провинция Аль-Анбар, Ирак;
Махмуд Л.А., Ассистент, Медицинский колледж Университета Анбар, г. Рамади, провинция Аль-Анбар, Ирак; Мохаммед М.А., Главное управление образования провинции Анбар, Министерство образования, провинция Аль-Анбар, Ирак; Аль-Рави Х.Ф., профессор, Научный колледж Университета Анбар, г. Рамади, провинция Аль-Анбар, Ирак; Аль-Хакейм Х.К., профессор, Научный колледж Университета Куфы, г. Куфа, провинция Наджаф, Ирак.
Поступила в редакцию 25.07.2022 Принята к печати 06.08.2022
Authors:
Saleh R.O., Medical Laboratory Techniques Department, Al-Maarif
University College, Ramadi, Al Anbar Governorate, Iraq;
Mahmood L.A., Assistent Professor, College of Medicine, University
of Anbar, Ramadi, Al Anbar Governorate, Iraq;
Mohammed M.A., General Directorate of Anbar Education, Ministry
of Education, Al Anbar Governorate, Iraq;
Al-Rawi K.F., PhD, Professor, College of Science, University of Anbar, Ramadi, Al Anbar Governorate, Iraq;
Al-Hakeim H.K., Professor, College of Science, University of Kufa, Kufa, Najaf Governorate, Iraq.
Received 25.07.2022 Accepted 06.08.2022
